Population sizes, HIV prevalence, and HIV prevention among men who paid for sex in sub-Saharan Africa (2000-2020): A meta-analysis of 87 population-based surveys.

BACKGROUND: Key populations, including sex workers, are at high risk of HIV acquisition and transmission. Men who pay for sex can contribute to HIV transmission through sexual relationships with both sex workers and their other partners. To characterize the population of men who pay for sex in sub-Saharan Africa (SSA), we analyzed population size, HIV prevalence, and use of HIV prevention and treatment. METHODS AND
FINDINGS: We performed random-effects meta-analyses of population-based surveys conducted in SSA from 2000 to 2020 with information on paid sex by men. We extracted population size, lifetime number of sexual partners, condom use, HIV prevalence, HIV testing, antiretroviral (ARV) use, and viral load suppression (VLS) among sexually active men. We pooled by regions and time periods, and assessed time trends using meta-regressions. We included 87 surveys, totaling over 368,000 male respondents (15-54 years old), from 35 countries representing 95% of men in SSA. Eight percent (95% CI 6%-10%; number of surveys [Ns] = 87) of sexually active men reported ever paying for sex. Condom use at last paid sex increased over time and was 68% (95% CI 64%-71%; Ns = 61) in surveys conducted from 2010 onwards. Men who paid for sex had higher HIV prevalence (prevalence ratio [PR] = 1.50; 95% CI 1.31-1.72; Ns = 52) and were more likely to have ever tested for HIV (PR = 1.14; 95% CI 1.06-1.24; Ns = 81) than men who had not paid for sex. Men living with HIV who paid for sex had similar levels of lifetime HIV testing (PR = 0.96; 95% CI 0.88-1.05; Ns = 18), ARV use (PR = 1.01; 95% CI 0.86-1.18; Ns = 8), and VLS (PR = 1.00; 95% CI 0.86-1.17; Ns = 9) as those living with HIV who did not pay for sex. Study limitations include a reliance on self-report of sensitive behaviors and the small number of surveys with information on ARV use and VLS.
CONCLUSIONS: Paying for sex is prevalent, and men who ever paid for sex were 50% more likely to be living with HIV compared to other men in these 35 countries. Further prevention efforts are needed for this vulnerable population, including improved access to HIV testing and condom use initiatives. Men who pay for sex should be recognized as a priority population for HIV prevention.

Introduction

Despite continued efforts to control HIV epidemics, 1.7 million new HIV infections occurred in 2019, with the greatest disease burden found in sub-Saharan Africa (SSA) [1]. In 2014, the Joint United Nations Programme on HIV/AIDS (UNAIDS) announced its objective to end AIDS by 2030 by considerably increasing diagnosis, treatment, and viral suppression among people living with HIV [2]. To achieve the HIV incidence reduction targets, interventions must prioritize key populations, which include sex workers, men who have sex with men, people who inject drugs, transgender people, and incarcerated people [3]. Key populations have unmet HIV prevention needs and contribute disproportionately to HIV transmission dynamics. Worldwide, over 60% of new adult HIV infections in 2019 were in individuals from key populations and their partners [1]. Even in high HIV prevalence settings, focusing HIV prevention approaches on key populations is important for limiting transmission [4].

Globally, sex workers experience a high HIV burden. Worldwide, an estimated 12% of female sex workers were living with HIV in 2011, reaching 37% in SSA [5]. The increased HIV acquisition risk among female sex workers is exacerbated by structural factors—including criminalization of sex work, stigma, and physical and sexual violence—which undermine sex worker engagement in HIV risk reduction behaviors and prevention [6-8]. Modeling studies suggest that the population attributable fraction of new HIV infections due to sex work ranges from less than 5% to 95%, depending on context [9-14]. This population-level impact is the result of chains of transmission linking sex workers and their clients to partners not involved in sex work [15].

Despite the central position of men who pay for sex in sexual networks, there has been comparatively little attention devoted to systematically reviewing representative epidemiological data on these men and on interventions focused on this population. Clients of sex workers are not designated, nor recognized, as a key population by UNAIDS, in part because of their lack of perceived structural vulnerabilities [1]. However, neglecting this population places the responsibility to prevent HIV transmission solely on sex workers. Developing appropriate interventions for clients of sex workers can be challenging and requires a granular understanding of the population sizes, sexual behaviors, HIV epidemiology, and uptake of HIV prevention interventions of this group. As with other key populations, clients of sex workers are hard to reach, and there can be wide variations in the definition of sex work [16]. Time–location surveys that collect information on clients of sex workers are often limited by their high non-response rates and lack of representativeness [17-19]. In contrast, nationally representative population-based surveys that collect information on paid sex may provide a promising alternative for characterizing men who pay for sex [20,21]. However, these surveys rely on self-reports, which are susceptible to underreporting of stigmatized behaviors such as paid sex [22].

The goal of this study is to improve our understanding of the complex HIV transmission dynamics arising from sex work. To achieve this, we first synthesize national population-based surveys conducted in SSA from 2000 to 2020 that collected information on paid sex ever. Second, we use meta-analyses to estimate population sizes, lifetime number of sexual partners, condom use, and HIV prevalence, testing, and treatment outcomes among men who do, and do not, pay for sex in SSA.

Methods

Data sources and selection criteria

We searched for nationally representative population-based surveys conducted in SSA over the time period 2000–2020 with available microdata on ever paying for sex (Table A S1 Text). Specifically, we considered Demographic and Health Surveys (DHS), AIDS Indicator Surveys (AISs) (https://dhsprogram.com/methodology/survey-types/ais.cfm), Population-based HIV Impact Assessment (PHIA) (https://phia-data.icap.columbia.edu/), Multiple Indicator Cluster Surveys (MICS) (https://mics.unicef.org/surveys), and other country-specific population-based surveys (e.g., Kenya AIDS Indicator Survey [KAIS] and South Africa National HIV Prevalence, Incidence, Behaviour and Communication Survey [SABSSM]; Table A in S1 Text). We included all available surveys and did not exclude based on survey language.

Variables of interest and definitions

We extracted data on paid sex (ever and past 12 months), lifetime number of sexual partners, condom use during last paid sex, HIV serostatus, HIV testing history (ever and past 12 months), antiretroviral (ARV) use (as determined by ARV biomarker data), and viral load suppression (VLS) among sexually active men. For most surveys, men were identified as having ever paid for sex if (1) they reported that any of their last 3 sex partners was a sex worker or (2) they reported either ever paying for sex or doing so in the past 12 months. Men who had never had sex were excluded.

Data analysis

Using respondent-level data from each survey, we calculated relevant estimands, along with their 95% confidence intervals (95% CIs), for men aged 15–54 years, accounting for complex survey designs (i.e., survey weights, stratification, and clustering). We did not pool estimates if denominators were smaller than 10. We pooled outcomes using inverse-variance-weighted random-effects meta-analysis with the empirical Bayes estimator for heterogeneity. We used I2 statistics to assess heterogeneity across estimates [23]. We calculated the following estimands: pooled proportions of men who paid for sex ever and in the past 12 months; pooled proportions of men who used a condom during their last paid sex; pooled proportions of men who ever tested for HIV; HIV prevalence among men who paid for sex; prevalence ratios (PRs) of HIV, HIV testing history (ever and past 12 months), ever HIV testing among people living with HIV, ARV use, and VLS among men who had paid for sex and those who had not; and mean and ratio of means (log-transformed) of lifetime number of sexual partners for men who had paid for sex and those who had not. Meta-analyses were performed on logit-transformed proportions and log-transformed PRs. Calculations were stratified by regions and by time periods (2000–2009 and 2010–2020). When calculating lifetime number of sexual partners and the PRs for HIV and HIV testing, we standardized results by age and urban/rural residence type.

We performed univariable meta-regression to assess whether the proportion of men who paid for sex, condom use at last paid sex, HIV testing, HIV prevalence, and PRs of HIV and HIV testing (ever and in the past 12 months) varied by survey year and whether the proportion of men who paid for sex varied by age and urban/rural residence type. Meta-regression was performed using logit-transformed proportions and log-transformed PRs, and we assessed time trends by using our models to estimate outcomes in 2010 and 2020. These analyses were not pre-registered. R software was used (4.0.0), and the DHS/AIS data were extracted using the rdhs package [24]. Survey data were analyzed with the survey package [25], and meta-analyses were performed using the metafor package [26]. This meta-analysis was reported in accordance with MOOSE guidelines [27].

Ethics

All analyses were performed on anonymized, de-identified data. DHS/AIS survey protocols were approved by the Institutional Review Board of ICF International in Calverton, MD, US, and other surveys (PHIA, KAIS, and SABSSM) were approved by the relevant country authorities. Ethics approval for secondary data analyses was obtained from McGill University’s Faculty of Medicine Institutional Review Board (A10-E72-17B).

Results

Description of included surveys

Our review identified 226 nationally representative population-based surveys, of which 87 (78 DHS/AISs, 6 PHIAs, 2 SABSSMs, and 1 KAIS) included information on men ever paying for sex. These surveys were conducted in 35 countries and included 368,283 unique sexually active male respondents aged 15 to 54 years (Fig 1). Together, these 35 countries represent 95% of men in SSA [28]. Survey questions were sufficiently standardized for them to be pooled (Table B in S1 Text). Twenty-six countries had more than 1 included survey, and the median year of data collection was 2012. Under two-thirds of surveys had information on HIV seroprevalence (number of surveys [Ns] = 52), but only 9% had information on ARV biomarkers (Ns = 8), and 10% on VLS (Ns = 9).

Fig 1

Surveys with questions about ever paying for sex, by country and year, 2000–2020.

Points represent population-based surveys conducted in sub-Saharan Africa from 2000 to 2020 and asking men about ever paying for sex. Circles represent surveys with data on HIV testing, while triangles represent surveys without data on HIV testing. Filled in points represent surveys that include HIV biomarker testing, while empty points represent surveys that did not have biomarker testing. AIS, AIDS Indicator Survey; DHS, Demographic and Health Surveys; KAIS, Kenya AIDS Indicator Survey; PHIA, Population-based HIV Impact Assessment; SABSSM, South African National HIV Prevalence, Incidence, Behaviour and Communication Survey.

All 87 surveys were included to calculate the proportion of men who reported ever paying for sex (Fig 2). Fifty-two surveys were included in analyses of HIV prevalence and PRs (Fig 2); the Zambia 2013–14 DHS survey was not included in this analysis because of concerns about the accuracy of the results of the HIV testing algorithm assay [29]. Surveys were included in other analyses based on inclusion of relevant questions.

Fig 2

Flowchart of population-based survey inclusion in each analysis.

A total of 226 population-based surveys conducted in sub-Saharan Africa from 2000 to 2020 were reviewed, and 87 were identified as having information on paid sex ever among men. Surveys were included in each analysis based on availability of relevant information.

Population size and lifetime number of sexual partners

The pooled proportion of sexually active men who reported ever paying for sex in SSA was 8.0% (95% CI 6.1%–10.3%, Ns = 87, I2 = 100%; Fig 3; Table C in S1 Text), with the highest proportions in Central and Eastern Africa, at 11.9% and 11.3%, respectively. Proportions were similar for surveys conducted from 2010 onwards (8.5%, 95% CI 6.4%–11.2%, Ns = 64, I2 = 100%) and before 2010 (6.7%, 95% CI 4.0%–10.9%, Ns = 23, I2 = 100%; Table D in S1 Text). There were no time trends in the proportion of men who paid for sex from 2000 to 2020 (Table E in S1 Text). Men residing in urban areas were more likely to report ever paying for sex (9.7%, 95% CI 7.3%–12.7%) than those from rural areas (7.1%, 95% CI 5.2%–9.6%; Table F in S1 Text). The pooled proportion of sexually active men who paid for sex in the past 12 months was 2.7% (95% CI 2.1%–3.5%, Ns = 79, I2 = 100%). Younger men (15–24 years) were more likely to report paying for sex in the past 12 months (5.1%, 95% CI 3.6%–7.1%) than those aged 35–54 years (2.2%, 95% CI 1.5%–3.2%; Table G in S1 Text).

Fig 3

Forest plots of proportions of sexually active men who ever paid for sex.

Data from 87 population-based surveys (78 DHS/AISs, 6 PHIAs, 2 SABSSMs, 1 KAIS) were collected and meta-analyses conducted to determine the proportion of men who have ever paid for sex. Pooled proportions were calculated for each region for both post-2010 (2010–2020) and pre-2010 (2000–2009) surveys, and overall. AIS, AIDS Indicator Survey; DHS, Demographic and Health Surveys; DRC, Democratic Republic of the Congo; KAIS, Kenya AIDS Indicator Survey; PHIA, Population-based HIV Impact Assessment; SABSSM, South African National HIV Prevalence, Incidence, Behaviour and Communication Survey.

Men who paid for sex had an average 12.0 lifetime sexual partners (standardized; 95% CI 10.9–13.1, Ns = 68, I2 = 100%). The average lifetime number of partners was highest in Central Africa (19.6, 95% CI 15.5–24.8, Ns = 9, I2 = 100%) and lowest in Eastern Africa (10.2, 95% CI 9.1–11.3, Ns = 24, I2 = 100%). Across all 4 regions, men who paid for sex had consistently more lifetime sexual partners, with an average 2.3 times more partners compared to men who did not pay for sex (standardized; 95% CI 2.1–2.4, Ns = 68, I2 = 100%; Fig C in S1 Text).

Condom use

Among men who reported paid sex in the past year, 62.2% used a condom the last time they paid for sex (95% CI 57.4%–66.7%, Ns = 84, I2 = 97%; Fig 4; Fig D in S1 Text), with the highest proportion in Southern Africa, at 76.6% (95% CI 65.8%–84.7%, Ns = 11, I2 = 88%), and lowest proportion in Eastern Africa, at 55.2% (95% CI 46.5%–63.6%, Ns = 35, I2 = 98%). We did not compare condom use at last paid sex with condom use at last sex among men who did not pay for sex because condom use depends strongly on partner type. Condom use at last paid sex increased over time (odds ratio per decade 1.07, 95% CI 1.04–1.11; estimate for 2010: 60.4%, 95% CI 55.8%–64.7%; estimate for 2020: 75.7%, 95% CI 70.6%–80.2%; Table E in S1 Text). Pooling by time period, condom use at last paid sex was higher for surveys conducted from 2010 onwards (67.5%, 95% CI 63.9%–70.9%, Ns = 61) than for surveys conducted before 2010 (46.6%, 95% CI 37.9%–55.4%, Ns = 23; Table D and Fig E in S1 Text).

Fig 4

Pooled estimates of HIV prevalence, condom use at last paid sex, HIV testing history, antiretroviral use, and viral load suppression among men who paid for sex, overall and stratified by sub-Saharan Africa regions.

Meta-analysis was performed for each outcome, and pooled proportions were calculated by region and overall. Analyses for HIV prevalence and HIV testing history were standardized by age and urban/rural residence type. PLHIV, people living with HIV.

HIV prevalence

The pooled HIV prevalence among men who paid for sex was 5.1% (95% CI 3.4%–7.5%, Ns = 52, I2 = 98%; Fig F in S1 Text). This varied greatly across regions and was highest in Southern Africa and lowest in Central Africa. The pooled, standardized PR between HIV among men who paid for sex and those who did not was 1.50 (95% CI 1.31–1.72, Ns = 52, I2 = 87%; Fig 5). Men who paid for sex were more likely to be living with HIV than men who did not pay for sex in all 4 regions, with the highest ratio in Western Africa (1.67, 95% CI 1.10–2.53, Ns = 18, I2 = 63%).

Fig 5

Forest plot of standardized HIV prevalence ratios for men who have ever paid for sex compared to men who have never paid for sex.

HIV biomarker data from 52 population-based surveys were collected and meta-analyses conducted to determine HIV prevalence ratios for men who have ever paid for sex compared to men who have not. Prevalence ratios are standardized by age and urban/rural residence type. Pooled prevalence ratios were calculated for each region and overall. DHS, Demographic and Health Surveys; DRC, Democratic Republic of the Congo; PHIA, Population-based HIV Impact Assessment; PLHIV, people living with HIV.

HIV prevalence among men who paid for sex decreased slightly over time (odds ratio per year 0.98, 95% CI 0.95–1.01; estimate for 2010: 5.5%, 95% CI 3.7%–8.2%; estimate for 2020: 3.6%, 95% CI 1.6%–8.1%), but uncertainty was large, and this finding was also consistent with a stable HIV prevalence (Table E in S1 Text). Pooled HIV prevalence was lower for surveys conducted from 2010 onwards compared to surveys conducted before 2010 (Table D in S1 Text). There was a small reduction with time in the HIV PR between men who paid for sex and those who did not (Table E in S1 Text).

HIV testing

Men who paid for sex were more likely to report having tested for HIV ever and in the past 12 months (Figs G and H in S1 Text) compared to those who did not pay for sex. The pooled, standardized PR for having ever tested for HIV was 1.14 (95% CI 1.06–1.24, Ns = 81, I2 = 98%), while the pooled, standardized PR for testing in the past 12 months was 1.09 (95% CI 1.00–1.18, Ns = 76, I2 = 95%). There were no time trends in the PR of HIV testing (Table E in S1 Text).

Across regions, men who paid for sex were generally more likely to have been tested for HIV compared to men who did not pay for sex. The PR of ever HIV testing was highest in Western Africa and lowest in Central Africa (Fig G in S1 Text), while the PR of testing in the past 12 months was highest in Central Africa and lowest in Eastern Africa (Fig H in S1 Text).

Among men who paid for sex, the pooled proportion ever tested for HIV was 34.4% (95% CI 26.9%–42.7%, Ns = 81, I2 = 99%; Table C in S1 Text). This proportion was highest in Southern Africa and lowest in Western Africa. Lifetime HIV testing among men who paid for sex increased over time (odds ratio per year 1.15, 95% CI 1.10–1.20; estimate for 2010: 31.9%, 95% CI 25.5%–39.1%; estimate for 2020: 64.9%, 95% CI 52.0%–75.9%; Table E in S1 Text).

Among those living with HIV, the proportion ever tested for HIV was similar among men who paid for sex and those who did not (PR 0.96, 95% CI 0.88–1.05, Ns = 18, I2 = 83%; Fig I in S1 Text). When pooled by region, men living with HIV who paid for sex were less likely to have ever tested for HIV than men living with HIV who did not pay for sex in all 4 regions, although all confidence intervals crossed the null (Fig I in S1 Text).

ARV use and VLS

Few surveys with sufficient denominators included information on ARV biomarkers (Ns = 8) and viral load (Ns = 9). For people living with HIV, there was no evidence of a difference in ARV biomarker coverage among men living with HIV who paid for sex compared to those who did not (PR = 1.01, 95% CI 0.86–1.18, Ns = 8, I2 = 71%; Fig J in S1 Text). Results were similar for VLS (PR = 1.00, 95% CI 0.86–1.17, Ns = 9, I2 = 51%; Fig K in S1 Text).

Discussion

In this study, we systematically analyzed 87 population-based surveys conducted over 2 decades in SSA. From 2000 to 2020, 8% of sexually active men in SSA reported ever paying for sex, and this proportion was higher in urban areas. Men who paid for sex were 50% more likely to be living with HIV compared to men who did not pay for sex, and only 68% of men in the last decade reported using a condom during their last paid sex. Men who paid for sex had a slightly higher probability of having ever tested for HIV, but ARV use and VLS among men who paid for sex were similar to those among men who did not pay for sex, although the evidence for ARV use and VLS were limited to fewer countries.

We found important regional variations in reports of paid sex. A higher proportion of men reported ever paying for sex in Central and Eastern Africa compared to Western and Southern Africa, which is consistent with a 2006 systematic review [16]. The overall proportion of sexually active men who paid for sex in the past 12 months was 2.7%, which is lower than an estimate of 4.3% from a review of 2010–2016 DHS surveys [30]. Differences can be explained by the present study including more surveys, representing more participants and 8 more countries. Also, our main analyses use lifetime measures of paid sex, which may be less prone to underreporting and social desirability bias than measures of recent paid sex. However, our population size estimates for clients of sex workers are probably lower bounds because of potential non-disclosure of paid sex. For example, the proportion of adult women engaged in sex work is estimated to range from 0.4% to 4.3% [31]. Given these numbers, it is unlikely that our estimate of 8% of men paying for sex would be sufficient to sustain this number of women engaging in sex work, although a study from Rwanda reported similar numbers of female sex workers and clients [32]. All surveys included here used face-to-face interviewer-administered questionnaires. Responses could be affected by social desirability bias, and this bias could be greater than for alternative confidential survey methods such as polling booth surveys [22].

Consistent with regional variations in population HIV prevalence, HIV prevalence among men who paid for sex was highest in Southern Africa and lowest in Central and Western Africa [1]. Men who paid for sex were more likely to be living with HIV than men who did not pay for sex, which is consistent with a 2008 analysis [33]. A similar finding was also found for female sex workers, whose odds of living with HIV were 12 times higher than that of all women aged 15–49 years [5]. HIV prevalence among men who paid for sex was lower in surveys conducted from 2010 onwards compared to surveys conducted before 2010, but uncertainty was large, and we cannot rule out that prevalence among this group remained stable. Although HIV prevalence was lowest in Western Africa, this region had the highest HIV PR comparing men who pay for sex with those who do not. In these settings, adding interventions that focus on the unmet prevention needs of men who pay for sex may be more cost-effective than those focused on the general population [34].

Men who paid for sex were more likely to have ever tested for HIV across regions and time. The PR for lifetime HIV testing between men who paid for sex and men who did not was highest in Western Africa and lowest in Central Africa. Higher risk perception encouraging testing, or greater availability of testing in areas with higher HIV burden, may explain this result [35,36]. Men living with HIV who paid for sex were equally likely to have been tested as those who did not paid for sex, which could have implications for knowledge of HIV status in this group. A recent study found that diagnosis coverage and time to diagnosis in SSA have drastically improved over the last decade, but in 2020 the largest group of individuals unaware of their status was men [37]. Distribution of HIV self-tests to sex workers, who can then distribute the tests to peers, clients, and partners, may further improve knowledge of status among men who pay for sex [38]. This approach is preferable to interventions such as index testing that require sex workers to disclose the identity of their male clients, which could put the sex workers at risk of violence, loss of sex work income, or both [6].

As men who pay for sex often also have female partners not involved in sex work, they may disproportionally contribute to population-level HIV transmission if virally unsuppressed [9,15,39]. Our results suggest comparable ARV use and VLS levels among men who paid for sex and men who did not. However, these estimates are based on a small number of surveys from 2012–2017, highlighting important data gaps. For SSA as a whole, the 2020 estimates of ARV use and VLS remain below UNAIDS targets, and men may be less likely to initiate and adhere to ARV treatment than women [1,40,41]. Treatment access can be facilitated by services targeted to men who are more likely to frequent sex workers, such as migrant laborers, long-distance truck drivers, mine workers, and other men who travel for work [42]. Improving access to treatment for men who pay for sex is especially important as, from 2010 onwards, only 68% of men used a condom the last time they paid for sex. A recent analysis of 29 DHS surveys from 2010–2019 found that, among men who reported condom use at their last paid sex, 84% reported consistent condom use during paid sex [43], which is higher than our estimates. Since the survey instruments only asked about consistent condom use if men reported condom use at last paid sex, we would expect consistent condom use measures to be higher. Altogether, these results suggest that, when men pay for sex and use condoms, they tend to do so consistently. Nevertheless, clients of sex workers often have decisive power over condom use during paid sex, and global evidence suggests higher HIV prevalence among clients of sex workers who do not use condoms [9,44,45]. For these reasons, continued condom use promotion in this population is strongly warranted.

Our results should be interpreted considering several limitations. First, population-based surveys of sexual behaviors depend on self-reports, so estimates could be affected by recall and social desirability biases [46-48]. Use of a lifetime measure of paid sex may have alleviated underreporting, but measures could still be underestimated, and, in the case of measures of association, this underestimation could attenuate the effect sizes towards the null. Confidential measures like polling booth surveys or audio computer-assisted self-interviews may improve accuracy [22,49,50]. Second, survey instruments captured men who have “paid” or, in a few instances, “given money, gifts, or favors in exchange” for sex. As money can be exchanged for sex outside of sex work, we cannot be certain that all men in our population are clients of sex workers. There are many sex work typologies, and transactional sex that involves exchanging gifts or favors may not have been reported as paid sex. For instance, relationships between male “sugar daddies” and younger women may be an important type of transactional sex that is probably not entirely captured in our surveys [51]. This could partly explain the smaller population size estimates for the Southern Africa region. For example, 18% of men in 2 South Africa provinces reported “ever having sex with a woman in prostitution,” but 66% reported having had some type of transactional sex [52]. Third, most surveys do not specify the paid partner’s gender when asking about paid sex, and we cannot be certain that all men in our analytical sample have paid for sex with a woman. However, the proportion of men who have sex with men in this region is estimated to be small [53]. Fourth, the included surveys had slightly different questionnaires and sampling strategies. Nevertheless, questions were largely similar, and using these multiple data sources allowed us to integrate information from more countries and respondents. Finally, few surveys had information on ARV treatment and VLS, so these estimates may not be generalizable to all regions.

Strengths of this study include our exhaustive analysis of all available population-based surveys with information on men who ever paid for sex in SSA, without restriction to any survey type. We synthesized new information on the epidemiology of HIV and the HIV prevention and treatment cascades among men who pay for sex. Our large sample size allowed investigation of patterns by regions and over time, and we estimated adjusted PRs using standardization to control for the effects of age and urban or rural area of residence.

Conclusion

Up to 1 in 10 men report ever paying for sex in SSA. To more accurately determine population sizes of men who pay for sex, improved confidential methods should be employed. Compared to those who have never paid for sex, men who have paid for sex have over double the lifetime number of partners, are more likely to be living with HIV, and, despite higher testing, could be less likely to know their status in some regions. Condom use initiatives and improved access to HIV testing campaigns are required to prevent HIV transmission from clients to sex workers and to their other sexual partners. These results suggest that men who pay for sex continue to constitute a distinct population subgroup at high risk of HIV acquisition and transmission, and that they should be recognized as a priority population for HIV prevention.

Additional information on the surveys, detailed forest plots, and complementary statistical analyses.

Table A: List of surveys considered and justifications for exclusion. Table B: Characteristics of population-based surveys conducted between 2000 and 2020 with available microdata included in analyses. Table C: Number of surveys, pooled estimates, confidence intervals, prediction intervals, and I2 values by region and overall for each outcome. Table D: Pooled estimates, confidence intervals, prediction intervals, and I2 for 2000–2009 and 2010–2020 for prevalence of paying for sex, condom use at last paid sex, and HIV prevalence and testing among men who have paid for sex. Table E: Results of univariate meta-regression for survey year. Table F: Pooled estimates, confidence intervals, prediction intervals, and I2 values for prevalence of paying for sex ever and in the past 12 months by urban/rural residence type. Table G: Pooled estimates, confidence intervals, prediction intervals, and I2 values for prevalence of paying for sex ever and in the past 12 months by age groups. Fig A: Flow charts of “HIV testing history” and “men who have ever paid for sex.” Fig B: Men ever paying for sex over time, by country. The proportion of sexually active men reporting ever paying for sex was calculated for 87 population-based surveys and plotted over time for countries with 3 or more surveys. Fig C: Bar graph of standardized mean lifetime number of sex partners for men who have paid for sex compared to men who have not, by survey. Fig D: Forest plot of proportion of men who paid for sex who reported condom use at last paid sex. Fig E: Condom use at last paid sex over time, by country. Fig F: Forest plot of standardized HIV prevalence for men who have paid for sex. Data from 52 population-based surveys was collected and meta-analysis conducted to determine HIV prevalence among men who reported having paid for sex. Prevalence is standardized by age and urban/ rural residence type. Proportions were pooled by region and overall. Fig G: Forest plot of standardized prevalence ratios for HIV testing ever among men who have paid for sex compared to men who have not. Fig H: Forest plot of standardized prevalence ratios for HIV testing in the last 12 months among men who have paid for sex compared to men who have not. Fig I: Forest plot of standardized prevalence ratios for HIV testing ever among men living with HIV who have paid for sex compared to men who have not. Fig J: Forest plot of prevalence ratios of antiretroviral use among men living with HIV who have paid for sex compared to men who have not. Fig K: Forest plot of prevalence ratios of viral load suppression among men living with HIV who have paid for sex compared to men who have not.

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15 Jun 2021

Dear Dr Maheu-Giroux,

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Dear Dr. Maheu-Giroux,

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Senior editor, PLOS Medicine

rturner@plos.org

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Comments from the reviewers:

*** Reviewer #1:

[See attachment]

Michael Dewey

*** Reviewer #2:

This is an excellent study that is adding to the small number of evidence on the clients of sex workers.

However, the authors did not address many of the issues they have highlighted. Using your critical minds and wealth of knowledge, what recommendations/ suggestions to you have for the public health authorities, the FSWs or to even clients of sex workers?

It would have been interesting to know:

-their policy recommendations for public health authorities?

- any recommendations for public health authorities using sex workers are index clients?

-what prevention approaches to tackle the high HIV prevalence in Western Africa versus the Southern?

-What services should be added in the health centers to reach out to clients of sex workers?

As a public health expert, this study is equality interesting to me as an evidence paper and as a study to guide policy recommendations. Unfortunately, the latter is missing.

*** Reviewer #3:

The study is very useful especially now that HIV prevalence remain stable in some countries in Sub Saharan Africa (SSA). It is appreciated to see that the authors have focused on a specific population group (Men) which provides more specific information regarding their vulnerability to HIV infections through pay for sex.

However, there are a few aspects that need to be incorporated:

- it is important to clarify starting with the abstract on which countries in SSA Africa the data was analyzed

- It is not clear on whether men who were involved in the study were heterosexual or not, I think the sexual orientation need to be stipulated clearly

- It is also important that the authors clarify in the abstract section about whether men paid for sex with women or with men? this is not clearly stated in the abstract

- An implication of the study on access to treatment will be useful

- Some additional lines that would contribute to the existing paradox that women also pay for sex (Mtenga et al. AIDS Res Ther (2018) 15:12 https://doi.org/10.1186/s12981-018-0199-6), will make a an important argument.

*** Reviewer #4:

I would like to appreciate the authors for the great work. Conceptually, I found the paper very coherent and structured in a well thought out fashion. With pleasure, I would like to provide a few suggestions on the manuscript.

First, motivation of the authors to do the paper has been mentioned as "less attention" drawn to interventions aimed at targeting clients of sex workers. However, this justification could not tell the convincing reason for doing the study as the study focuses on HIV epidemiology, including other related issues, among these groups. If you find that interventions on men paying for sex are given less attention, then the study should have been on finding reason for "why this happens". But now, your study is on HIV burden, HIV testing, condom use etc, and the driver for carrying out this should have been explained differently like for instance, limited knowledge base on this area. Simply put, linking a study focused on HIV burden with a justification using a statement like inattention to prevention [of HIV among those groups] did not work here. Interventions might not be given attention but as the same time HIV burden can be known. So, this leads us to that, there might be many other important factors that potentially prevent policy makers and intervention designers from drawing attention to interventions, and lack of knowledge on HIV epidemiology [the other issues this study has addressed] may still NOT be one of the factors.

So, I suggest that it would be highly useful to succinctly explicate the direct contribution of the paper towards reducing men running into the paid sex practice as well as to our knowledge base on this area.

Second, the selection process of the papers lacks some clarity. How did the authors restrict surveys between 2010-2020, for instance. It is also good to use PRIMSA adopted to the IPD, Individual Participant Data meta-analysis.

Third, the chosen of the random effect model was not justified; why not fixed effect model rather. Give reason. I found the I-squared statistics to be extremely high, suggesting considerable between-study variation in terms of the variables you studied. Given this variability, do you think producing a single overall estimate through pooling is a good practice, as the papers are too different already. Meta-analysists often try fitting meta-regression to explain the variation; in this study, meta-regression could have been done better by including more confounding variables in the model to get unbiased results. Also, under variables, make it clear whether all of these are outcome variables, and mention your independent variables, if any.

Fourth, of the three design elements of a complex design study like DHS, the authors take into account only clustering and unequal probability of selection, via, weighting. However, stratification is missing, and failing to take this problem into account would result in estimation of standard errors that are biased. DHS experts highly recommend researchers to make account of all the three design elements of complex designs like DHS. I would see as a big limitation of the paper as your confidence intervals are likely to be biased.

Finally, these days, countries are moving towards ensuring health equality within their population. International agreements like SDG calls for equity; that means, while the time is to look at health outcomes between different population groups within a country, your study was about aggregating country-level information into a regional level. How do you see the implication of the paper for the wellbeing of different segments of population in each individual country in light of SDG?

Within country inequality analysis of HIV prevalence among men paying for sex is very important to understand who these group of men are and why they are being engaged in the activity. Because, all men in this high-risk behavior are not homogenous population group and how their engagement in this risky sexual behavior is affected by contexts they live in remains a huge research question that could substantially contribute towards the SDG related with HIV. To the contrary, you did aggregate based analysis. Can explain more on this.

Regards,

Gebretsadik Shibre

***

Any attachments provided with reviews can be seen via the following link:

[LINK]

Submitted filename: hodgins.pdf

Click here for additional data file.

15 Sep 2021

Submitted filename: Response to reviewers.pdf

Click here for additional data file.

12 Oct 2021

Dear Dr. Maheu-Giroux,

Thank you very much for re-submitting your manuscript "HIV prevalence, population sizes, and HIV prevention among men who paid for sex in sub-Saharan Africa (2000-2020): a meta-analysis of 87 population-based surveys" (PMEDICINE-D-21-02581R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by one reviewer. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

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Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

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If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org.

We look forward to receiving the revised manuscript by Oct 19 2021 11:59PM.

Sincerely,

Beryne Odeny,

PLOS Medicine

plosmedicine.org

------------------------------------------------------------

Requests from Editors:

1. Please define abbreviations in tables e.g. PLHIV.

2. Please indicate in the figure caption the meaning of the bars and whiskers in Figure S4.

3. Please provide your MOOSE checklist and complete it with paragraph numbers per section (e.g. "Methods, paragraph 1").

4. The survey flowchart can be included in the main paper.

5. To help us extend the reach of your research, please provide any Twitter handle(s) that would be appropriate to tag, including your own, your coauthors’, your institution, funder, or lab.

Comments from the Academic Editor:

I would ask the authors to comment in a bit more detail the issue of 'paying' for sex as opposed to 'gifts etc'. There are some sentences relating to that in the limitation, which are fine, but for me the issue would be the overall rather low prevalence of men paying for sex (only 8% overall), which is lower than would be anticipated in terms of 'bought' sex (irrespective of whether it was money or gifts), especially in South/southern Africa. The authors may like to comment on the prevalence of paying for sex, by region. In South/southern Africa the concept of 'sugar daddy' is relatively common (and may involve more than one recipient at a time), but usually does not involve money directly and it is likely that this would not have been reported as 'paying for sex with a sex worker'. I appreciate that the authors are limited by the data available but could still comment on that in the limitations, and the generalisability of the conclusion.

Comments from Reviewers:

Reviewer #1: The authors have addressed all my points

Michael Dewey

Any attachments provided with reviews can be seen via the following link:

[LINK]

4 Nov 2021

Dear Dr Maheu-Giroux,

On behalf of my colleagues and the Academic Editor, Dr. Marie-Louise Newell, I am pleased to inform you that we have agreed to publish your manuscript "HIV prevalence, population sizes, and HIV prevention among men who paid for sex in sub-Saharan Africa (2000-2020): a meta-analysis of 87 population-based surveys" (PMEDICINE-D-21-02581R3) in PLOS Medicine.

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To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Thank you again for submitting to PLOS Medicine. We look forward to publishing your paper.

Sincerely,

Beryne Odeny

PLOS Medicine